The modern era of manufacturing has recognized the importance of a sustainable supply chain management (SCM) system for any industry in order to attain the desired level of stability and productivity for fulfillment of the customers’ requirements. Selection of the most suitable set of suppliers is an integral part of SCM which can be effectively solved with the deployment of different multi-criteria decision making (MCDM) techniques. This paper endeavors to resolve the uncertainty involved in the decision making process of supplier selection with the application of D numbers. A relatively new MCDM technique in the form of measurement alternatives and ranking according to compromise solution (MARCOS) is later employed for ranking of a set of alternative suppliers. This integrated approach is finally applied to choose the best performing supplier in a leading Indian iron and steel making industry based on seven selective evaluation criteria and opinions of three decision makers. It would provide more generic and unbiased results while addressing the uncertainty and ambiguity involved in the supplier selection process.

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An integrated D-MARCOS method for supplier selection in an iron and steel industry

The preference ranking organization method for enrichment of evaluations (PROMETHEE) method considers a significant outranking class of multi-criteria decision analysis (MCDA), as it is easy to deal with its simple computations. In the PROMETHEE, different preference functions are used according to the type and nature of attributes or criteria that demonstrate the clearness and reliability of this method. This study provides a new version of the PROMETHEE method using bipolar fuzzy information, named the bipolar fuzzy PROMETHEE method. Bipolar fuzzy sets or numbers constitute an asymmetrical relationship between two judgmental factors of human reasoning. Vague and imprecise knowledge is characterized by bipolar fuzzy linguistic terms which are further represented in the form of trapezoidal bipolar fuzzy numbers. The trapezoidal bipolar fuzzy numbers are used by analysts to assign the preferences of alternatives on the basis of criteria. Further, a ranking function of bipolar fuzzy numbers is considered to access the crisp real preferences of alternatives. The entropy weighting information is employed to calculate the weights of attributes by considering the condition of normality. A numerical example such as the selection of green suppliers by using the bipolar fuzzy PROMETHEE is performed on the basis of the usual criterion preference function in order to explain the procedure of the proposed method. Comparable results are derived by using the combination of linear and level preference functions. The results obtained by using different types of preference functions are the same, representing the authenticity of the proposed bipolar fuzzy PROMETHEE method.

Multi-Criteria Group Decision-Making for Selection of Green Suppliers under Bipolar Fuzzy PROMETHEE Process

Multi-criteria decision-making in the selection of a suitable biomass material for maximum bio-oil yield during pyrolysis

Due to stringent governmental regulations and increasing consciousness of the customers, the present day manufacturing organizations are continuously striving to engage green suppliers in their supply chain management systems. Selection of the most efficient green supplier is now not only dependant on the conventional evaluation criteria but it also includes various other sustainable parameters. This selection process has already been identified as a typical multi-criteria group decision-making task involving subjective judgments of different participating experts. In this paper, a green supplier selection problem for an automobile industry is solved while integrating the Cloud model with the technique for order of preference by similarity to an ideal solution (TOPSIS). The adopted method is capable of dealing with both fuzziness and randomness present in the human cognition process while appraising performance of the alternative green suppliers with respect to various evaluation criteria. This model identifies green supplier S 4 as the best choice. The derived ranking results using the adopted model closely match with those obtained from other variants of the TOPSIS method. The Cloud model can efficiently take into account both fuzziness and randomness in a qualitative attribute, and effectively reconstruct the qualitative attribute into the corresponding quantitative score for effective evaluation and appraisal of the considered green suppliers. Comparison of the derived ranking results with other MCDM techniques proves applicability, potentiality and solution accuracy of the Cloud TOPSIS model for the green supplier selection.

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A cloud topsis model for green supplier selection

Application of an MCDM model with data mining techniques for green supplier evaluation and selection

The PROMETHEE is a significant method for evaluating alternatives with respect to criteria in multi-criteria decision-making problems. It is characterized by many types of preference functions that are used for assigning the differences between alternatives in judgements. This paper proposes a preference of green suppliers using the PROMETHEE under the usual criterion preference functions. Comparable results are presented to check the effect of different preference functions on the final preference. Seven economical and environmental criteria, four suppliers and five decision makers were the main structures in the green supplier selection problem. Data were collected via personal communication with decision makers using five-point Likert scale. The algorithm of PROMETHEE under usual criterion function was implemented, and the results show that supplier A1 is the most preferred alternative. Comparative results also show that supplier A1 is the most preferred alternative despite the difference in preference functions used.

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Waimun Chan

Papers

Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions